In factories, plants, and the like, in order to operate production facilities and the like safely and normally, certain actions are necessary for preventing the occurrence of failures, accidents, and other defects in advance by constantly monitoring a state of the facility. Such actions are generally called preventive maintenance. As a system for implementing preventive maintenance, in the related art, a system configured to monitor data obtained from sensors and detect a change in the state of a facility or an indication of an abnormality has been proposed (refer to Patent Literatures 1 and 2). However, these systems of the related art have the following problems.
In the existing systems, in order to accurately ascertain a change in state or an indication of an abnormality from sensing data, it is necessary to build a model with high accuracy by learning from a large amount of data and adjusting parameters. Therefore, several months would be taken for system import before the operation starts.
In addition, there may be a time lag from when any change occurs in a state of a facility until a significant change appears in physical quantity that is monitored by a sensor. In such a case, a certain amount of time may have already elapsed after a defect occurs at a time at which an abnormality is detected in a system. Then, for example, in the case of a production facility, even if the facility is suspended at a time at which an abnormality is detected, a defective product may have already be produced and there is a need to for retroactive inspection. Moreover, since it is not possible to identify when the defect has occurred in the facility, all inspection has to be performed on a lot-by-lot basis.
Furthermore, to the existing systems require to acquire physical quantities related to various facilities as sensing data, and highly specialized knowledge is necessary to appropriately realize such sensing procedures.